# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import print_function

import unittest
from op_test import OpTest

import numpy as np
import paddle.fluid as fluid
import paddle.fluid.core as core
import warnings


def execute(main_program, startup_program):
    if core.is_compiled_with_cuda():
        place = core.CUDAPlace(0)
    else:
        place = core.CPUPlace()
    exe = fluid.Executor(place)
    exe.run(startup_program)
    exe.run(main_program)


class TestDeviceGuard(unittest.TestCase):
    def test_device_guard(self):
        main_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(main_program, startup_program):
            data1 = fluid.layers.fill_constant(
                shape=[1, 3, 8, 8], value=0.5, dtype='float32')
            data2 = fluid.layers.fill_constant(
                shape=[1, 3, 5, 5], value=0.5, dtype='float32')
            shape = fluid.layers.shape(data2)
            with fluid.device_guard("cpu"):
                shape = fluid.layers.slice(
                    shape, axes=[0], starts=[0], ends=[4])
                with fluid.device_guard("gpu"):
                    out = fluid.layers.crop_tensor(data1, shape=shape)
        # check if the device attr is set correctly
        all_ops = main_program.global_block().ops
        device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
        for op in all_ops:
            if op.type == 'slice':
                self.assertEqual(op.desc.attr(device_attr_name), "cpu")
            if op.type == 'crop_tensor':
                self.assertEqual(op.desc.attr(device_attr_name), "gpu")

        execute(main_program, startup_program)

    def test_cpu_only_op(self):
        main_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(main_program, startup_program):
            x = fluid.layers.fill_constant(
                shape=[2, 255, 13, 13], value=0.3, dtype='float32')
            gt_box = fluid.layers.fill_constant(
                shape=[2, 6, 4], value=0.5, dtype='float32')
            gt_label = fluid.layers.fill_constant(
                shape=[2, 6], value=1.0, dtype='int32')
            gt_score = fluid.layers.fill_constant(
                shape=[2, 6], value=0.5, dtype='float32')
            anchors = [
                10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156,
                198, 373, 326
            ]
            anchor_mask = [0, 1, 2]
            with fluid.device_guard("gpu"):
                # yolov3_loss only has cpu kernel, so its cpu kernel will be executed
                loss = fluid.layers.yolov3_loss(
                    x=x,
                    gt_box=gt_box,
                    gt_label=gt_label,
                    gt_score=gt_score,
                    anchors=anchors,
                    anchor_mask=anchor_mask,
                    class_num=80,
                    ignore_thresh=0.7,
                    downsample_ratio=32)

        execute(main_program, startup_program)

    def test_without_kernel_op(self):
        main_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(main_program, startup_program):
            i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
            loop_len = fluid.layers.fill_constant(
                shape=[1], dtype='int64', value=10)
            cond = fluid.layers.less_than(x=i, y=loop_len)

            with warnings.catch_warnings(record=True) as w:
                warnings.simplefilter("always")
                with fluid.device_guard("cpu"):
                    while_op = fluid.layers.While(cond=cond)
                    with while_op.block():
                        i = fluid.layers.increment(x=i, value=1, in_place=True)
                        fluid.layers.less_than(x=i, y=loop_len, cond=cond)

        assert len(w) == 1
        all_ops = main_program.global_block().ops
        device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
        for op in all_ops:
            if op.type == 'while':
                self.assertEqual(op.desc.attr(device_attr_name), "")

        execute(main_program, startup_program)

    def test_error(self):
        def device_attr():
            with fluid.device_guard("cpu1"):
                out = fluid.layers.fill_constant(
                    shape=[1], value=0.2, dtype='float32')

        self.assertRaises(ValueError, device_attr)

    def test_warning(self):
        main_program = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(main_program, startup_program):
            with warnings.catch_warnings(record=True) as w:
                warnings.simplefilter("always")
                with fluid.device_guard("gpu"):
                    x = fluid.layers.fill_constant(
                        shape=[1], value=3.0, dtype='float32', force_cpu=True)
                    y = fluid.layers.fill_constant(
                        shape=[1], value=4.0, dtype='float32')
                    result = fluid.layers.less_than(x=x, y=y, force_cpu=False)

        assert len(w) == 2
        all_ops = main_program.global_block().ops
        device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
        for op in all_ops:
            self.assertEqual(op.desc.attr(device_attr_name), "gpu")


if __name__ == '__main__':
    unittest.main()